Sources of energy production in Europe

Mekko stacked

Photo by Michael Fousert on Unsplash

Bergen, Norway

Behind the clouds, the sky is always blue…
— Norwegian Proverb

Ingest

country, energy type, and production output by year

df = read_csv("archetypes/energy-consumption-by-country/energy.csv")
df

Wrangle & Analyze

summarize country, energy type, and value

This operation will simplify the data to three energy type groups: Renewable, Nuclear, and Conventional Thermal.

df_final <- df %>%
  mutate(
    type = fct_other(
      type,
      keep = c("Nuclear", "Conventional thermal"),
      other_level="Renewable"
    ),
    type = fct_relevel(type, "Renewable", "Nuclear", "Conventional thermal")
  ) %>%
  select(country, country_name, type, value=`2018`) %>%
  group_by(country, country_name, type) %>%
  summarize(value = sum(value)) %>%
  ungroup() 
  
df_final

Wrangle & Analyze

calculate percent of clean energy

df_country_sort <- df_final %>%
  mutate(type = fct_other(type,
                          keep = c("Conventional thermal"),
                          other_level="non_co2_pollutant")) %>%
  group_by(country, type) %>%
  summarise(upper_value = sum(value)) %>%
  ungroup() %>%
  group_by(country) %>%
  mutate(total = sum(upper_value)) %>%
  ungroup() %>%
  filter(!type == "Conventional thermal") %>%
  mutate(percent_clean = 100 * upper_value / total) %>%
  arrange(desc(percent_clean)) %>%
  select(country, percent_clean)
  
df_country_sort

Wrangle & Analyze

calculate percent production

df_country_production <- df_final %>%
  group_by(country) %>%
  summarise(prod_by_country = sum(value)) %>%
  ungroup() %>%
  group_by() %>%
  mutate(prod_europe = sum(prod_by_country)) %>%
  ungroup() %>%
  group_by(country) %>%
  summarize(percent_production = 100 * prod_by_country / prod_europe) %>%
  ungroup() %>%
  arrange(desc(percent_production))

df_country_production

Wrangle

transform from long to wide

df_production_by_type <- df_final %>%
  pivot_wider(names_from = type, values_from = value)

df_production_by_type

Wrangle & Analyze

merge production type and country, calculate percentages

We’ll now have all the needed measure for each energy type and the percent of production vs percent clean.

df_join <- inner_join(df_production_by_type, df_country_production) %>%
  inner_join(df_country_sort) %>%
  arrange(desc(percent_clean)) %>%
  mutate(xmax=cumsum(percent_production),
         xmin=xmax-percent_production) %>%
  mutate(total_country_energy = Renewable + Nuclear + `Conventional thermal`,
         percent_renewable = 100 * Renewable / total_country_energy,
         percent_nuclear = 100 * Nuclear / total_country_energy,
         percent_thermal = 100 * `Conventional thermal` / total_country_energy)

df_join

Plot

visual variables mapped

theme_opts <- theme(
  text = element_text(family = "inconsolata", size = 16), 
  plot.title = element_text(color = "black", size = 16, face = "bold"),
  plot.subtitle = element_text(color = "black", size = 12),
  plot.caption = element_text(color = "#555555", size = 11),
  panel.border = element_blank(),
  plot.background = element_blank(),
  panel.background = element_blank(),
  panel.grid.major.x = element_blank(),
  panel.grid.minor.x = element_blank(),
  panel.grid.major.y = element_blank(),
  panel.grid.minor.y = element_blank(),
  legend.title = element_blank(),
  axis.title.x = element_blank(),
  axis.title.y = element_blank(),
  axis.text.x = element_blank(),
  axis.text.y = element_blank(),
  axis.ticks.x = element_blank(),
  axis.ticks.y = element_blank(),
  legend.position = "top"
)

fill_colors <- c("Renewable" = "#FF9800", 
                 "Nuclear" = "#F8EE5D", 
                 "Conventional thermal" = "#D2D2D2")

v1 <- ggplot(df_join) +
  geom_rect(aes(ymin = 0, ymax = percent_nuclear, xmin = xmin, xmax = xmax,
                fill = "Nuclear"),
                color = "#FFFFFF") + # F6EC3A
  geom_rect(aes(ymin=percent_nuclear, ymax=percent_nuclear + percent_renewable,
                xmin = xmin, xmax = xmax,
                fill = "Renewable"),
            color = "#FFFFFF") + # EABB26
  geom_rect(aes(ymin = 0, ymax = -percent_thermal,
                xmin = xmin, xmax = xmax,
                fill = "Conventional thermal"),
            color = "#FFFFFF") + # C4C4C4
  scale_fill_manual(values = fill_colors) +
  geom_text(aes(x = (xmin+xmax)/2,
                y = percent_nuclear + percent_renewable,
                label = country),
                size = 3,
                vjust = -0.5, hjust = 0.5, family = "inconsolata") +
  labs(title = "How European countries generated electricity in 2018") +
  theme_bw() +
  theme_opts

girafe(ggobj = v1, width_svg = 16, height_svg = 9,
       options = list(opts_sizing(rescale = TRUE, width = 0.8)))

Plot

with annotations

offset <- 2

v2 <- v1 +
  geom_segment(aes(x=-4, xend=106, y=100, yend=100), colour = "#EEEEEE", size = 0.5, linetype="dotted") +
  geom_segment(aes(x=-4, xend=106, y=75, yend=75), colour = "#EEEEEE", size = 0.5, linetype="dotted") +
  geom_segment(aes(x=-4, xend=106, y=50, yend=50), colour = "#EEEEEE", size = 0.5, linetype="dotted") +
  geom_segment(aes(x=-4, xend=106, y=25, yend=25), colour = "#EEEEEE", size = 0.5, linetype="dotted") +
  geom_segment(aes(x=-4, xend=106, y=0, yend=0), colour = "#EEEEEE", size = 0.5, linetype="dotted") +
  geom_segment(aes(x=-4, xend=106, y=-25, yend=-25), colour = "#EEEEEE", size = 0.5, linetype="dotted") +
  geom_segment(aes(x=-4, xend=106, y=-50, yend=-50), colour = "#EEEEEE", size = 0.5, linetype="dotted") +
  geom_segment(aes(x=-4, xend=106, y=-75, yend=-75), colour = "#EEEEEE", size = 0.5, linetype="dotted") +
  geom_segment(aes(x=-4, xend=106, y=-100, yend=-100), colour = "#EEEEEE", size = 0.5, linetype="dotted") +
  annotate("text", x = -4, y = (0 + offset), label = "0%", vjust = 0, hjust = 0, family = "inconsolata") +
  annotate("text", x = -4, y = (-25 + offset), label = "25%", vjust = 0,hjust = 0, family = "inconsolata") +
  annotate("text", x = -4, y = (-50 + offset), label = "50%", vjust = 0,hjust = 0, family = "inconsolata") +
  annotate("text", x = -4, y = (-75 + offset), label = "75%", vjust = 0,hjust = 0, family = "inconsolata") +
  annotate("text", x = -4, y = (-100 + offset), label = "100% conventional thermal energy", vjust = 0, hjust = 0, family = "inconsolata") +
  annotate("text", x = 106, y = (0 + offset), label = "0%", vjust = 0, hjust = 1, family = "inconsolata") +
  annotate("text", x = 106, y = (25 + offset), label = "25%", vjust = 0, hjust = 1, family = "inconsolata") +
  annotate("text", x = 106, y = (50 + offset), label = "50%", vjust = 0, hjust = 1, family = "inconsolata") +
  annotate("text", x = 106, y = (75 + offset), label = "75%", vjust = 0, hjust = 1, family = "inconsolata") +
  annotate("text", x = 106, y = (100 + offset), label = "clean energy 100%", vjust = 0, hjust = 1, family = "inconsolata") +
  annotate("text", x = 1, y = 118, 
           label="Norway had an electricity production almost entirely made up of renewable\nenergy (97%). It is the second largest producer of this type of energy in Europe.\n \u2193", 
           vjust = 1.0, hjust = 0, family = "inconsolata") +
  annotate("text", x = 50, y = 65, 
           label="Germany is the larger energy producer in\nEurope and also produce the most renewable\nand conventional thermal energy (representing\n31% and 56% of production, respectively.)\n         \u2193", 
           hjust = 0, family = "inconsolata") +
  annotate("text", x = 0, y = -13, 
           label="France is the second largest \u2191\nenergy producer in Europe\nbut by far the largest nuclear energy\nprovider representing 71% of its production.", 
           vjust = 1.0, hjust = 0, family = "inconsolata") +
  annotate("text", x = 71, y = -85, 
           label="Most of Poland's energy production is  \u2192\nfrom conventional thermal energy (90%).", 
           vjust = 1.0, hjust = 0, family = "inconsolata")

girafe(ggobj = v2, width_svg = 16, height_svg = 9,
       options = list(opts_sizing(rescale = TRUE, width = 0.8)))

References

The citations and data sources used for this case

Narrative: Eric Ekholm, TidyTuesday, GO
Data: Eurostat, GO